code: def __sample_sentence(self, doc, sent):
      old_topic = sent.topic
      num_topics = self.__model.num_topics()
      accum_prob = np.zeros(num_topics)
      prob = np.zeros(num_topics)
      sum_ = 0
      for t in range(num_topics):
          dt_alpha = doc.topic_sum(t) + self.__model.alpha()
          t_sum_beta_sum = (
              self.__model.topic_sum(t) + self.__model.beta_sum()
          )
          if t == old_topic:
              if dt_alpha > 1:
                  dt_alpha -= 1
              if t_sum_beta_sum > 1:
                  t_sum_beta_sum -= 1
          prob[t] = dt_alpha
          for i in range(len(sent.tokens)):
              w = sent.tokens[i]
              wt_beta = (
                  self.__model.word_topic_value(w, t)
                  + self.__model.beta()
              )
              if t == old_topic and wt_beta > 1:
                  wt_beta -= 1
              # Note: if the length of the sentence is too long, the probability will be
              # too small and the accuracy will be lost if there are too many multiply items
              prob[t] *= wt_beta / t_sum_beta_sum
          sum_ += prob[t]
          accum_prob[t] = (
              prob[t] if t == 0 else accum_prob[t - 1] + prob[t]
          )
  
      dart = rand() * sum
      if dart <= accum_prob[0]:
          return 0
      for t in range(1, num_topics):
          if accum_prob[t - 1] < dart <= accum_prob[t]:
              return t
      return num_topics - 1
  
imports: import os
  import numpy
  from tqdm import tqdm
  from paddlehub.common.logger import logger
  from lda_novel.document import LDADoc, SLDADoc, Token, Sentence
  from lda_novel.vose_alias import VoseAlias
  from lda_novel.util import rand, rand_k
label_desc: Use image node_id 4 for calling the GibbsSampler obj's underlying member method code with example usage: obj.__sample_sentence(doc, sent) and returns: unknown, int, t